A3: SPL Library Checkouts
Introduction
An introduction of the data and a description of the trends/books/items you are choosing to analyze (and why!)
The dataset that I chose to analyze is the dataset with items checked out at least 5 times a month from 2013-2023. I chose this because I want to check the trends for publisher, and what author, subject of the book over time, to see how the audience preference has changed over time, and see what is currently the most appealing to them.
Summary Information
The Seattle Public Library dataset provides insightful values for visitors’ preferences, such as the authors, publishers, and books that got checked out the most, as well as annual and monthly check out trends.
The most frequently checked out author is Williams Mo, with a total of 326710 checkouts.
The book that has been checked out the most is Educated a memoir, by Tara Westover with an incredible 17,817 total checkouts
The most checked out publisher is Random House, Inc., with a total of 1,830,160 checkouts
Between the year 2005-2023, the year with the most amout of checkouts was 2019, with the total amount of 2,626,271 checkouts
January is the the month with the most amount of checkouts with the total amount of 2,537,907 checkouts
The Dataset
Seattle open data collected and published data from the Seattle public library, which includes the monthly count of checkouts at the library. Although this program to release data from the library only started in 2017, there was an employee at the library who was collecting this data since 2005, to display to visitors in the library what was getting checked out the most, now the data being collected become of an initiative by Barack Obama to have more open data to the public.
The dataset collects usage class (physical or digital, checkout type, material type (book, movie, etc.), check out year and month, total checkout amounts, and the title, creators, and subject of each piece, as well as the publisher and publication year. There are a total of 42 million rows in this dataset, however, this report will focus on checkouts with a book checkout type, and with books that were checked out more than ten times.
When working with this data, analysts need to make sure that they are not excluding medium and creators. They need to make it explicit that they are only working with a certain type of data, to ensure that the data being represent is a completely accurate picture. The main problem with this dataset is that since it’s very large, there can be lots of values that are missing, and since there are so many rows detecting those values is challenging, thus the best way to work with this data is to select a small portion of it and analyze that.
First Trends Over Time Chart
Include a chart. Make sure to describe why you included the chart, and what patterns emerged
The first chart that you will create and include will show the trend over time of your variable/topic/interest. Think carefully about what you want to communicate to your user (you may have to find relevant trends in the dataset first!). Here are some requirements to help guide your design:
- Show more than one, but fewer than ~10 trends
- For example, two books, or 5 different mediums
- You must have clear x and y axis labels
- The chart needs a clear title
- You need a legend for your different line colors and a clear - legend title In your .Rmd file, make sure to describe why you included the chart, and what patterns emerged
When we say “clear” or “human readable” titles and labels, that means that you should not just display the variable name.
Here’s an example of how to run an R script inside an RMarkdown file:
Second Trends Over Time Chart
Include a chart. Make sure to describe why you included the chart, and what patterns emerged
The second chart that you will create and include will show another trend over time of your variable/topic/interest. Think carefully about what you want to communicate to your user (you may have to find relevant trends in the dataset first!). Here are some requirements to help guide your design:
- Show more than one, but fewer than ~10 trends
- For example, two books, or 5 different mediums
- You must have clear x and y axis labels
- The chart needs a clear title
- You need a legend for your different line colors and a clear - legend title In your .Rmd file, make sure to describe why you included the chart, and what patterns emerged
When we say “clear” or “human readable” titles and labels, that means that you should not just display the variable name.
Here’s an example of how to run an R script inside an RMarkdown file:
Your Choice
The last chart is up to you. It could be a line plot, scatter plot, histogram, bar plot, stacked bar plot, and more. Here are some requirements to help guide your design:
- You must have clear x and y axis labels
- The chart needs a clear title
- You need a legend for your different line colors and a clear legend title
Here’s an example of how to run an R script inside an RMarkdown file: